{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import theano\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import MDBN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding a layer with 559 input and 40 outputs\n",
      "Adding a layer with 19937 input and 400 outputs\n",
      "Adding a layer with 400 input and 40 outputs\n",
      "Adding a layer with 1686 input and 200 outputs\n",
      "Adding a layer with 200 input and 20 outputs\n",
      "Adding a layer with 100 input and 24 outputs\n",
      "Adding a layer with 24 input and 3 outputs\n"
     ]
    }
   ],
   "source": [
    "import AMLsm2\n",
    "reload (AMLsm2)\n",
    "me_DBN, ge_DBN, sm_DBN, dm_DBN, top_DBN = AMLsm2.load_network('Run_2016-12-21_0840/Exp_2016-12-21_0840_run_0.npz','../MDBN_run')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datafiles = AMLsm2.prepare_AML_TCGA_datafiles('../data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ME_output, _ = me_DBN.MLP_output_from_datafile(datafiles['ME'], datadir='../data')\n",
    "GE_output, _ = ge_DBN.MLP_output_from_datafile(datafiles['GE'], datadir='../data')\n",
    "SM_output, _ = sm_DBN.MLP_output_from_datafile(datafiles['SM'], datadir='../data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 99.5, 169.5, -0.5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SqqejZnlt2Jyilm4zyhXu3StCg+yB5athx0UEfVLVRsHma/VUiqzaK+LlEk1EE6UCGBq8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10600d710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "joint_layer = np.concatenate([ME_output, GE_output, SM_output],axis=1)\n",
    "plt.imshow(joint_layer, cmap='gray',interpolation='none')\n",
    "plt.axis('tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x11a073110>,\n",
       "  <matplotlib.axis.XTick at 0x11a05fe90>,\n",
       "  <matplotlib.axis.XTick at 0x11a0b76d0>],\n",
       " <a list of 3 Text xticklabel objects>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZeYK0pDHgUyQrs2wD7pG0OiIe6GhzOnB0RBwr6bXAZ4CT8vTZJXhmufkbS1PkrJM+Edgc\nEVsAJK0ClgAPdLRZAtwIEBF3SzpY0ryI2D7Xg7oEr8Vcv2u2t5x10kcAD3e8foQkcM/WZmu6rbwg\nLWk+yZlhHsnq4NdGxCckHQLcDCwgWTH8zIjYkb5nKXA+sAu4KCLWdtu3Z8Er2bgXSK3CZbt31t2F\n1lsxVsx+eqU7Jie2sGViSzEHKViW08ou4C8i4j5JzwO+K2ktcB5we0R8RNLFwFLgEkmvAM4EFpEs\neX67pGOjrmXJzcxSvYL0kYuP4sjFR+15vW7ZHd2abQVe0vF6frptZpsj+7QZSN8gHRGPAY+lz5+U\ntCk98BLglLTZDcAEcAlwBrAqInYBk5I2k3wluHvmvleMN2+9sWZxrrQKK8b2r7sLltHT+dY4vAc4\nRtIC4FHgLODsGW3WAO8FbpZ0EvBvefLRMGBOWtJC4NXAt4E9yfCIeEzSoWmzI4C7Ot42nZPptseB\nOmuD8peXKvjaSvmKu5ll7jnpiJiSdCGwlmdL8DZJuiD5cVwTEV+R9BZJPyYpwTsvb58z9zhNdXyR\nJMf8pKSZEWDgiHDZ1DODvsUGsGLcI7wqeKjRHHnrpCPia8BxM7Z9dsbrC3MdZIZMQVrSfiQB+vMR\nsTrdvH26tETSYcDj6fbMOZkV43d2vFqYPqw4Dh9V8M0sZZhMH9PWFbLXNt8W/jlgY0R8vGPbGuBc\nYCXwTmB1x/abJF1NkuY4BvhO990uHrS/ZjYSFrL3oK2YIN3K+aQlnQz8GbBB0r0kaY1LSYLzLZLO\nB7aQVHQQERsl3QJsBHYC7+lV2eFcXrlcJ10Vf2NpilbOJx0Rd9J73ek39XjPFcAV/fbtOw7NrEpt\nTneUxCOQcrm6owr+Rli+on7Dz+QrwatF88b+ZmZz1MqcdJk8WXq5nE6qhr8PNkcrc9LWZA4fVXAJ\nXhWGYz7pOtQapD3BUsma93lsKJ8Mm8JB2sxsiDknPSBPsGRmVXJOekAuXSqXLxxWw/NJl6+o+aRd\ngmc2guSUdGM0Md1R0PnJzGz4TbFfpsegJB0iaa2kByXdKungLm3mS/qGpPslbZD0viz7dnVHmzVv\n0NBIY+o7A4INiRKrOy6hy0pVM9p0XeWqc7XxbmoN0uPjV9Z5+BHgnHQVnO1ojhKDdK+VqvboscrV\nEey92vg+nJM2s5FRYpA+tMdKVV11rHK1z7KCMzlIm9nIeJoD5/xeSbcB8zo3kcxi9sEuzXvObjZz\nlat+x601SLtErGz+Il4F3xZehXJvC39q4h6emlg/63sj4s29fiap10pVM9t1W+VqVuoxH3/pJIU/\n2uXypP/WHsuIiFyjDklxdPwwU9t/0SsHOp6klcAvImJleuHwkIjYpzJC0o3AzyLiL7Lu29Udbebq\nDrO9lFgn3XWlKkmHA9dGxFt7rXKVLm7bU705ad8FYGYVKuu28Ij4BV1WqoqIR4G3ps9nW+WqJ184\nNLOR4VnwBrRibP86Dz8CvHxWFTwHTfmK+g23MkhLmg/cSFJ6shu4JiI+KenDwLt59irmntyKpKXA\n+SR32FwUEWu77dsf7nK5eqYau3f72krpxoq58e3pZ9o5wVK3WxlvS392VURc1dlY0iKSpPkiYD5w\nu6Rjo64yErOSjRUUQKx8U7ual+Ht2+NZbmWE7oW4S4BVEbELmJS0GTiRDHfWmJmVaWpXC9MdnWbc\nyvg64EJJ7wDWAx+IiB0kAfyujrdt5dmgvhd/Hbc2cI1Sc7Q6SM+8lVHSp4HlERGSLgc+CrxrkIM7\nJ10u38xSDd9xWIViYsWunS0N0t1uZYyIn3Y0uRb4cvp8K3Bkx8/mp9v2MdHxfGH6sCL5MkAVPNgo\n3mT6mLauoP3unmphTjr1OWBjRHx8eoOkw9J8NcDbgen7LdcAN0m6miTNcQzwnW47XTyXHptZ6y1k\n70FbUUGaBqY7+s7dkd7KuA7YQDI0C+BS4ByS/PRukpPeBdNT9aUleP8N2EmPEjxJ4fmOy+ZsaTX8\njaV8ywuZu4MHM/5bHafcxytKrRMs4ZypmWVSzARL3J8x3v3O8ATpmlcLX1bn4VvPFw7NZthVdwcG\n17wsupnZXDlID8Z10mZWqZ3l7FbSIcDNwAKSa3RnpveNdGs7RnJvySMRcUbffXvS//bySbAaLsEr\n33IoJid9Z8Z4d/JgOel00v+fd6wW3nXS/7Tt+4H/ALwgS5B2uqPVhuK6R+v5ZFiFgk6E5aU7+q4W\nDnsmrHsLsALItDqLg7SZjY7flLbnrKuFXw38T+DgrDt2kDbLzd9YGiPHSDrvauGS/hOwPZ1RdDEZ\nPzi+cNhqvsmiCs5Jl6+w33CvIL1hAn44MetbC1gt/GTgDElvAQ4Cni/pxoj489mO6wuHLeY66ar4\nZFi+gu44/FLGf6s/ndOFw76rhXe0P4Vk5lBfODQrn9MdjVFSCR4ZVguf644dpM1sdEyVs9ssq4XP\n2P4t4FtZ9u0g3WKXTT1ddxdGwvi4l88qW+k56SHmIN1iK8abt+hmEznz3yDlleCVpuZZ8HzpsFzO\nlVbDFw7LV9CFw7/J+G/1Xs+CZ2ZWPac7BjUUJ6oW8wivCq6TLt8o56RdJ91irpOuik+G5Sso3XFl\nxn+rS5zuAHzHobXFUPwtWxYlleCVyTlpMxsdDazucE7azEZHA3PSfYO0pANJVgs/IH2sjohLZ1uJ\nIF0t/HySX0nX1cLBaxyWzTlpsxnKuy28NH2DdEQ8LenUiHhK0jhwp6STgTOA2ztWIlgKXCLpFST3\nrS8C5gO3Szo2ulyhdE7azCrVwJz0WJZGEfFU+vTA9D1PkKxEcEO6/QbgbenzM4BVEbErIiaBzcCJ\nRXXYzGzOdmV8DJFMOel04cTvAkcDn4mIjdNzp8I+KxEcAdzV8fat6bZ9uL60XP6mUg1/jss3ynXS\nmYJ0ROwGfl/SC4Bb01UFZqYvBi4WdRCxNvDl7wZpY066U0T8UtJXgBOAXisRbAWO7Hjb/HTbPl7f\ncX5cmD6sOL5wWI1lHmyUYDJ9TFtXzG5LmhhytkKKGe0OBv4WeCWwGzg/Iu6ebd99c9KSXpTuGEkH\nAW8G7gXWAOemzd4JrE6frwHOknSApJcCxwDf6bbvxR2Phf06YmYjZCF7R4iClJeTvoSkkOI44Bsk\nhRTdfBz4SkQsAn4P2NRvx1lG0ocDN0gSSVD/fER8XdK9dFmJIM1X3wJsJPly8Z5ulR0AU1M9V5ex\nIozX3YFR4YRHY5SX7lgCnJI+vwGYIAnce6Tp4v8YEecCRMQu4Jf9dpylBG8DcHyX7V1XIkh/dgVw\nRb99e7L0sjndYbaX8krwDu1RSNHppcDPJF1PMopeT3Ifya9n27FvC281T/xTBVd3lK/06o6fTcDP\nJ2Z9q6TbgHmdm0j+yD7YpXm3P779SAa8742I9ZI+RjLannU0VesseJc53VGqFeMH1t2FEeGTYfkK\nmgXv9Iz/Vl8deLXwTcDijkKKb6Z5584284C7IuKo9PXrgIsj4k9m23emm1nMzFphZ8bH4HoVUuyR\npkMelvSydNMbSa7dzaredId8wcXawJ/jxihvbeaVdCmkkHQ4cG1ETK8Y/j7gJkn7Az8Bzuu3Y0/6\n32K+WagazkmXbzkUk+74g4zx7q7hmfTfC9G22lB8xkaAc9LlKygnfULGf6v1wxOkXd1hZqOjgbPg\nOd3RYk53VMPpjvIVlu743YzxbsPwjKRrDdK+1aJc/hJeDZ8Mq1BQumNRxr+KTQ7Szklba3gkXb7C\nRtLHZIx3Px6eIO2ctJmNjvJK8ErjIN1iHuFVw+mOKhT0WW7rpP9lcRApl4NHVYbiW7Fl0fZJ/4vm\nIGJtcNnuZ+ruQuutKGoCiwaW4DndYWajw+mOwTjdUS5/U6nG+FjfqdNtWDhI23BxrrQKPhlWoaAB\nXQNz0q6TNsvJ3wjLV1iddOZbvIanTtrzSZuZ5STpEElrJT0o6dbpxbu7tFsq6X5JP5B0k6QD+u7b\nc3e013KvcVgR34BfvoJuCy9pJC1pJfDziPiIpIuBQyJi5kK0C4BvAi+PiGck3Qz8U0TcONu+++ak\nJR0IrAMOSB+rI+JSSR8G3g08nja9NCK+lr5nKXA+SZr+oohY223fzuVZOwzFt2KrV9/VwklWBn8G\neK6k3cBvAdv67TjLauFPSzo1Ip6SNA7cKenk9MdXRcRVne0lLSJZlWARMB+4XdKxUdeQfYR9iGV1\nd2Ek+BtLk5R25bDvauER8YSkjwIPAU8BayPi9n47zlTdERFPpU8PJMljP5G+7jaEWAKsiohdwKSk\nzcCJwN0zG/qCS7n8TaUaPhmWr7hI0asGb1366C3vauGSjgLeDywAdgBflHRORHxhtuNmCtKSxoDv\nAkcDn4mIjUrWJ7xQ0juA9cAHImIHcARwV8fbt6bbzMxq1msk/QfpY9pf79MiIt7ca6+Stkua17Fa\n+ONdmp0A3BkRv0jf8w/AHwL5g3RE7AZ+X9ILgLWSTgE+DSyPiJB0OfBR4F1Z9mdVca60Cv7GUoWi\nxtK/Lmg/+5heLXwlPVYLBx4E/lLSc0jm43sjcE+/HQ90M0tE/FLSPwEnRMS3On50LfDl9PlW4MiO\nn81Pt+1jOa/veLUwfVhR/DXcmmoyfUybPRExiNJy0n1XC4+I70u6kSQrMQXcC1zTb8d9S/AkvQjY\nGRE7JB0E3AosA+6PiMfSNu8HXhMR50h6BXAT8FqSNMdtwD4XDl2CVz6P8Kw9iirB+9eMrV86NDez\nZBlJHw7coCQJPQZ8PiK+LulGSa8GdpOc9C4ASPPVtwAbSU5b73Flh5kNh+bdF56lBG8DcHyX7X8+\ny3uuADzrTO2GYiBgNkSaN8OS55NuMeekq+E66SZp4UjazKw9SqvuKI2DtJmNEKc7BnLZVPO+ejTJ\n8nGnk8z21ryYU2uQXjG+f52Hbz3fdl8N56SbxCPpgTiIlMsXZs1m8kjazGyIeSRtNnJc6li+4r5z\neyRtQ8U3s1TBaaUqDP0ES6XxzSwt5hFeNXzhsEk8kjYzG2LOSQ/E1R3l8jcVs5nKGUlL+i/AX5Es\nG/iaiPhej3anAR8jmazuuohY2W/fTncMZJJmzXndxJz0JM36HTfVJKP5ey5tJL0B+M/AZ3s1SFe4\n+hTJZP/bgHskrY6IB2bbsUfSA5gAFtfch0E0M1c6yWgGj6pNMpq/53JG0hHxIEA6pXMvJwKbI2JL\n2nYVyZqwwxukmzeSnmBdg8J0Ey8cTgCL+Va/ZkOlmSfDUVVrTvoI4OGO14+QBO5Z1Rqkjz/+8DoP\nP7Bt257Pi1/cnD4fvu804EPv+du2cfiLX1x3NwZyPM35TEzbtu15jfosf69rhncu5l6CN8tq4ZdF\nxJe7vyu/vstnlXZgyau1mFlmBSyfNQksyNh8e0QcNodjfBP4QLcLh5JOAv4qIk5LX18CRL+Lh7WN\npIdl/TAzGw0RsbCiQ/WKbfcAx0haADwKnAWc3W9nYwV2zMxsJEl6m6SHgZOAf5T01XT74ZL+ESAi\npoALgbXA/cCqiNjUd99eI9bMbHh5JJ2BpNMkPSDpR5Iurrs/bSTpOknbJf2g7r60laT5kr4h6X5J\nGyS9r+4+WX8eSfeRFqD/iI4CdOCsfgXoNhhJrwOeBG6MiFfV3Z82knQYcFhE3CfpecB3gSX+LA83\nj6T721OAHhE7gekCdCtQRNwBPFF3P9osIh6LiPvS508Cm0hqd22IOUj3160A3R9sazRJC4FXA3fX\n2xPrx0HabMSkqY4vAhelI2obYg7S/W0FXtLxen66zaxxJO1HEqA/HxGr6+6P9ecg3d+eAnRJB5AU\noK+puU9tJZo5dV+TfA7YGBEfr7sjlo2DdB9zLUC3wUj6AvB/gZdJekjSeXX3qW0knQz8GfAGSfdK\n+l46v7ENMZfgmZkNMY+kzcyGmIO0mdkQc5A2MxtiDtJmZkPMQdrMbIg5SJuZDTEHaTOzIeYgbWY2\nxP4/m4P+7ql7ATwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116edf410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_output = top_DBN.get_output(theano.shared(joint_layer,borrow=True))\n",
    "plt.imshow((top_output>0.8)*np.ones_like(top_output)-(top_output<0.2)*np.ones_like(top_output),interpolation='none',extent=[0,3,385,0])\n",
    "plt.colorbar()\n",
    "plt.axis('tight')\n",
    "plt.xticks(np.arange(0.5,3.5,1),('0','1','2'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x116edf390>,\n",
       "  <matplotlib.axis.XTick at 0x11a0baf90>,\n",
       "  <matplotlib.axis.XTick at 0x1115a9f50>],\n",
       " <a list of 3 Text xticklabel objects>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iL3kyMSCKVFt+FHhU0juKNlqk2vKzkvaNiKckTQeul7R3/uMzJkZ6STsDhwA7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\nQdjMLCEHYTOzhByEzcwSchA2M0vo/wM2r2PZrm6I9AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a0ba490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(top_output, interpolation='none',extent=[0,3,385,0])\n",
    "plt.axis('tight')\n",
    "plt.colorbar()\n",
    "plt.xticks(np.arange(0.5,3.5,1),('0','1','2'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([array([ 54.,  10.,   4.,   3.,   5.,   2.,   2.,   2.,   6.,  82.]),\n",
       "  array([ 153.,    0.,    0.,    0.,    0.,    1.,    0.,    0.,    0.,   16.]),\n",
       "  array([ 99.,   3.,   0.,   0.,   1.,   1.,   3.,   5.,   1.,  57.])],\n",
       " array([  7.14913614e-11,   1.00000000e-01,   2.00000000e-01,\n",
       "          3.00000000e-01,   4.00000000e-01,   5.00000000e-01,\n",
       "          6.00000000e-01,   7.00000000e-01,   8.00000000e-01,\n",
       "          9.00000000e-01,   1.00000000e+00]),\n",
       " <a list of 3 Lists of Patches objects>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zprr/Hs3LYaRq8AF4ki8AL1XVr85ruw14uapuS/JrwGlVdfNxtq2jxz777Ldy4MDdLBn4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\nl6RGGPiS1Ij/Bx/R8KPitmOqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1174a6bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(top_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 99.,   3.,   0.,   0.,   1.,   1.,   3.,   5.,   1.,  57.]),\n",
       " array([  2.82781603e-08,   1.00000025e-01,   2.00000023e-01,\n",
       "          3.00000020e-01,   4.00000017e-01,   5.00000014e-01,\n",
       "          6.00000011e-01,   7.00000008e-01,   8.00000006e-01,\n",
       "          9.00000003e-01,   1.00000000e+00]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ht8WTnjL1IadkyJ+RryW5AbiDpT8qs7Oq7upx7IkY8vviauDaJLezFMM/r6oH+5t6MpJ8\nDJgDTk9yH0ufTjqJjt30xCtJaoT/ibkkNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1Ij\n/h/05nU6cvp9KwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1116a1650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(top_output[:,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "code = (top_output[:,0:3] > 0.5) * np.ones_like(top_output[:,0:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from utils import find_unique_classes\n",
    "U = find_unique_classes(code)\n",
    "cl = U[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  1.,  0.,  0.,  0.,  5.,  1.,  4.,  1.,  4.,  5.,  4.,  0.,\n",
       "        4.,  4.,  4.,  4.,  2.,  5.,  1.,  1.,  5.,  5.,  5.,  5.,  1.,\n",
       "        5.,  5.,  1.,  5.,  4.,  2.,  3.,  0.,  5.,  4.,  0.,  5.,  0.,\n",
       "        1.,  4.,  5.,  4.,  1.,  0.,  0.,  4.,  4.,  0.,  1.,  2.,  4.,\n",
       "        5.,  1.,  0.,  4.,  4.,  5.,  4.,  2.,  1.,  0.,  4.,  0.,  0.,\n",
       "        4.,  5.,  4.,  1.,  4.,  4.,  0.,  5.,  5.,  5.,  5.,  4.,  4.,\n",
       "        2.,  4.,  5.,  5.,  4.,  0.,  4.,  1.,  0.,  1.,  5.,  0.,  4.,\n",
       "        0.,  1.,  5.,  4.,  0.,  0.,  5.,  5.,  5.,  5.,  5.,  0.,  4.,\n",
       "        0.,  0.,  1.,  4.,  5.,  1.,  4.,  5.,  0.,  0.,  4.,  0.,  5.,\n",
       "        4.,  4.,  1.,  4.,  4.,  7.,  5.,  0.,  1.,  4.,  0.,  0.,  4.,\n",
       "        0.,  5.,  1.,  4.,  0.,  4.,  5.,  0.,  4.,  5.,  0.,  5.,  6.,\n",
       "        1.,  2.,  4.,  1.,  5.,  4.,  1.,  4.,  0.,  2.,  5.,  4.,  3.,\n",
       "        4.,  4.,  2.,  2.,  0.,  2.,  4.,  5.,  4.,  2.,  4.,  0.,  4.,  2.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 37.,  24.,  13.,   2.,  52.,  40.,   1.,   1.]),\n",
       " array([ 0.   ,  0.875,  1.75 ,  2.625,  3.5  ,  4.375,  5.25 ,  6.125,  7.   ]),\n",
       " <a list of 8 Patch objects>)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ne95eWFXns/LXxFu74cUWbALOB97b5X8C2DNspMklOQ14HfCxccvOqsT/G3j+Ec93dvN0\ngnRjgTcDH66qW4fOs1bdn8L/ALx86CwTuBB4XTeu/LfAq5J8aOBME6mqR7t/vwncwsoQaQseAR6u\nqi92z29mpdRb8zvAl7qv/3HNqsTvBH4xyZlJfgp4PdDcEXra3YsC+CBwX1W9Z+ggk0ry3CRbu+nT\ngUuAu4dN1V9VXVNVz6+qF7Ly2j9QVX84dK6+kpzR/RVHkmcBvw38x7Cp+qmqZeDhJGd3sy4CWhuK\nA3gDPYZSYEb3TjkZLgRK8lFgEfiZJF8Hrj18sGTeJbkQeCNwTzeuXMA1VfXPwybr7XnAvqzcj3MD\nK39NfHrgTKeSBeCW7pYZm4Abqmr/wJkmcRVwQzck8TXgTQPnmUiSM1g5qPmWXst7sY8ktcsDm5LU\nMEtckhpmiUtSwyxxSWqYJS5JDbPEJalhlrgkNcwSl6SG/T8uSUxEwgWAMQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1117e5e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(cl,bins=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check Survival curves for the different classes\n",
    "==============================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "id=[]\n",
    "with open('../data/'+datafiles['ME']) as f:\n",
    "    my_csv = csv.reader(f,delimiter='\\t')\n",
    "    id = my_csv.next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stat={}\n",
    "with open('../data/AML/AML_clinical_data2.csv') as f:\n",
    "    reader = csv.reader(f, delimiter=',')\n",
    "    for row in reader:\n",
    "        patient_id=row[0]\n",
    "        stat[patient_id]=(row[4],row[7],row[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following case IDs were  not found in clinical data\n",
      "No data for TCGA-AB-2887\n",
      "No data for TCGA-AB-2891\n",
      "No data for TCGA-AB-2918\n",
      "No data for TCGA-AB-2921\n",
      "No data for TCGA-AB-2930\n",
      "No data for TCGA-AB-2940\n",
      "No data for TCGA-AB-2943\n",
      "No data for TCGA-AB-2946\n",
      "No data for TCGA-AB-2975\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "time_list = []\n",
    "event_list = []\n",
    "group_list = []\n",
    "print('The following case IDs were  not found in clinical data')\n",
    "for index, key in enumerate(id[1:]):\n",
    "    m = re.match('TCGA-\\w+-\\d+', key)\n",
    "    patient_id = m.group(0)\n",
    "    if patient_id in stat:\n",
    "        patient_stat = stat[patient_id]\n",
    "        add_group = True\n",
    "        try:\n",
    "            time_list.append(float(patient_stat[2]))\n",
    "            event_list.append(1)\n",
    "        except ValueError:\n",
    "            try:\n",
    "                time_list.append(float(patient_stat[1]))\n",
    "                event_list.append(0)\n",
    "            except ValueError:\n",
    "                print('No data for %s' % patient_id)\n",
    "                add_group = False\n",
    "        if add_group:\n",
    "            group_list.append(cl[index])\n",
    "    else:\n",
    "        print(patient_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112b2ec10>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sx8XAf4TbSW/nvcAKd/9e5FhaP9OD2priz7U6vVPC/wDbCN4Urwbm1vqN7iDb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\ngriISIIpiIuIJJiCuIhIgimIi4gk2P8HcgdpOWgfII4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112b1bcd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifelines import KaplanMeierFitter\n",
    "kmf = KaplanMeierFitter()\n",
    "kmf.fit(time_list,event_observed=event_list)\n",
    "kmf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XX1/PVVddxZ49e5gwYQIul4vhw4dz6623snnzZmbOnMmCBQu46667GD9+PKtWrQpNcnXk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\nkno+TtPi4cSm+Wm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O0CCuVI44zpezXQXlQPo4RSmlHEyDuFJKOZgGcaWUcjAN4kop5WAa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112cf7490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "T=np.array(time_list)\n",
    "E=np.array(event_list)\n",
    "ix = (np.array(group_list) == 0)\n",
    "kmf.fit(T[ix], E[ix], label='group 0')\n",
    "ax=kmf.plot()\n",
    "for i in range(1,6):\n",
    "    ix=(np.array(group_list)==i)\n",
    "    kmf.fit(T[ix], E[ix], label='group %d' % i)\n",
    "    kmf.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
